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Why you should care about debugging machine learning models

O'Reilly Media - Data

For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. Model debugging is an emergent discipline focused on finding and fixing problems in ML systems. We’ll review methods for debugging below. Not least is the broadening realization that ML models can fail.

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Have we reached the end of ‘too expensive’ for enterprise software?

CIO

What began with chatbots and simple automation tools is developing into something far more powerful AI systems that are deeply integrated into software architectures and influence everything from backend processes to user interfaces. While useful, these tools offer diminishing value due to a lack of innovation or differentiation.

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Shinkei Systems’ AI-guided fish harvesting is more humane and less wasteful

TechCrunch

There’s a far superior alternative, but it’s time-consuming and manual — but Shinkei Systems has figured out a way to automate it, even on the deck of a moving boat and has landed $1.3 million to bring its machine to market. That is, unless you automate it, which is what Shinkei Systems has done.

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Artificial Intelligence – A Guide for Thinking Humans

Henrik Warne

I don’t have any experience working with AI and machine learning (ML). In symbolic AI, the goal is to build systems that can reason like humans do when solving problems. This idea dominated the first three decades of the AI field, and produced so called expert systems. One such set is Image Net, consisting of 1.2

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Harness the power of MCP servers with Amazon Bedrock Agents

AWS Machine Learning - AI

AI agents extend large language models (LLMs) by interacting with external systems, executing complex workflows, and maintaining contextual awareness across operations. Whether youre connecting to external systems or internal data stores or tools, you can now use MCP to interface with all of them in the same way.

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The State of the Octoverse: machine learning

Github

In our 2018 Octoverse report, we noticed machine learning and data science were popular topics on GitHub. We decided to dig a little deeper into the state of machine learning and data science on GitHub. Julia, R, and Scala all appear in the top 10 for machine learning projects but not for GitHub overall.

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Machine Learning at scale: first impressions of Kubeflow

OpenCredo

Machine learning has great potential for many businesses, but the path from a Data Scientist creating an amazing algorithm on their laptop, to that code running and adding value in production, can be arduous. Here are two typical machine learning workflows. Monitoring. Does it only do so at weekends, or near Christmas?